How do I select a big data solution for my business?
Since big data consists of structured and unstructured data which is constantly growing in size, common software doesn’t have the ability to process and manage it. That’s why choosing the right big data solution is essential to make a data-driven organization function safely and thrive.
To select a suitable big data solution for your business, you need to think about a variety of factors.
We’ve talked to several industry professionals to get their insight on the topic.
Tido Carriero, Chief Product Development Officer, Twilio Segment
Understanding your customers is one of the most powerful applications of big data in the modern enterprise. But since the volume of data generated by customer interactions is growing exponentially, businesses need a solution in place to manage it.
Customer data platforms (CDPs) are a good option, as they clean and unify all your customer data, as well as centralising it in one place. This avoids data silos and makes it easy to channel data into the applications where you want to activate it.
The best investment is one that benefits many teams, from marketing to finance, so look for a flexible CDP that can channel data into multiple applications.
Don’t get locked into a single vendor’s product suite. The best CDPs allow you to plug your data into whichever tools work best for you, and let you switch tools on and off as your business evolves, making them a solid long-term investment.
Look out for CDP providers that encrypt data at rest and in transit, in line with the latest recommendations from the European Data Protection Board.
A good CDP will also automatically detect and classify your customer data, so you can easily comply with data privacy regulations and enforce data privacy policies at scale.
Hardik Chheda, VP Product Management, OmniSci
Regardless of the amount of data available to us or the sophistication of any machine learning model, no businesses could have predicted the disruption we’ve faced in the last 15 months. Proactively adopting big data technologies allows businesses (and society) to deal with disruptive change by accelerating their capabilities to anticipate, shift and respond.
Businesses of all sizes, across all industries now realize the benefit of big data as it’s no longer an asset available to only a select few [technical] folks due to the following dimensions:
Scale with speed
You shouldn’t have to choose between scale and speed. Big data systems should allow every user to extract insight from large datasets (billions) at the speed of curiosity without downsampling, indexing, pre-aggregation, and lag.
Scale with interactivity
You should be able to visually explore multi-billion records with rich interactive analytics such as cross-filtering, brushing, drilling, slicing-n-dicing, cohorts, etc., in real-time (milliseconds) without any pre-prep.
Scale with convergence
Big data platforms need to break down silos by uniting native SQL, Analytics, Data Science, and Location Intelligence workflows to answer all questions in a single place: what, where, when, why and how.
Scale with openness
The big data platform should have native interfaces to BI, GIS, ML, and data warehouse ecosystems to reap the benefits of the open ecosystem.
Mark Do Couto, SVP, Data Analytics, Altair
When considering a big data solution, companies must assess their own workforce and be reminded that employees need a solution that will allow easy access to data and ease of use in analyzing that data.
Data solutions must be seamlessly utilized by those of all skill sets – from skilled data scientists to citizen data scientists. The more people are given the means to mine and cross-examine data, the more likely it is that they will pose the critical questions and generate compelling answers.
Many organizations are considering open-source solutions as some of their talent pool may have experience coding in Python; it is crucial that big data solutions accommodate both current and future employees. Additionally, identifying a data platform that empowers users to easily leverage data preparation, data science, and visualization is important because it makes the process of validation a lot easier.
Finally, it is important for the solution to support a low-code / no-code environment as this will help eliminate barriers with a centralized and secure collaborative space for diverse teams to solve complex problems. The application of big data content is applied in multiple business lines, so it is important to keep all talent levels in mind.
Jonathan Sander, Security Field CTO, Snowflake
We’re seeing many organizations migrating to big data platforms to reap the windfalls of machine learning and analytics, but they are often challenged by security and governance issues.
These organizations come in all sizes, but the journey is very similar. They start with large, traditional, on-premises platforms that are unable to handle new data types and growing workload demands. This prompts a new project to seek a better platform, but security and governance is often neglected in the initial set of requirements.
While this new technology may meet some business needs, it is particularly challenged when moving important workloads with sensitive information. Most end up in a siloed environment with split workloads, only growing the personnel and maintenance required to manage these systems, as well as increasing the attack surface.
To ensure organizations drive the most value from their big data platforms, only consider systems that have mature security and governance baked into the platform’s foundation; disqualify any systems that don’t offer these assurances. The most successful organizations have learned this, have repositioned their requirements, and lead with security and governance.